In today's business intelligence world, On-Line Analysis Processing (OLAP) plays an important role as it extracts analytical information out of detailed transaction data. The data model used in OLAP is characterized by multiple dimensions and the hierarchy structure in each dimension.
The traditional multidimensional OLAP (MOLAP) approach uses special data structures, such as multi-dimension arrays, to store the precalculated aggregate data so it can deliver impressive query performance. But as the amount of data increases, scalability turns to be a big challenge. Relational OLAP (ROLAP) is becoming the choice for large data warehouses because of its ability to scale with large amount of data and its integration with other components in the enterprise intelligence architecture.
To achieve fast query response time, ROLAP materializes the precalculated aggregate data in table format, such as an aggregate join index (AJI). Different terminologies may be used to refer to the same data structure in a RDBMS, such as materialized view, automatic summary table etc. An optimizer decides whether an AJI can be used to answer an OLAP query based on a set of criteria. As with non-aggregate JIs, AJIs result in a need for extra storage and maintenance overhead.